NVIDIA CUDA toolkit for Linux and Windows contains a vulnerability in cuobjdump, where an attacker may cause an out-of-bounds read by tricking a user into running cuobjdump on a malformed input file. A successful exploit of this vulnerability may lead to limited denial of service, code execution, and limited information disclosure.

NVIDIA CUDA toolkit for Linux and Windows contains a vulnerability in cuobjdump, where an attacker may cause an out-of-bounds read by tricking a user into running cuobjdump on a malformed input file. A successful exploit of this vulnerability may lead to limited denial of service, code execution, and limited information disclosure.

NVIDIA CUDA toolkit for Linux and Windows contains a vulnerability in cuobjdump, where an attacker may cause an out-of-bounds memory read by running cuobjdump on a malformed input file. A successful exploit of this vulnerability may lead to limited denial of service, code execution, and limited information disclosure.

NVIDIA nvJPEG library contains a vulnerability where an attacker can cause an out-of-bounds read by means of a specially crafted JPEG file. A successful exploit of this vulnerability might lead to information disclosure or denial of service.

NVIDIA CUDA toolkit for all platforms contains a vulnerability in the nvdisasm binary, where a user could cause an out-of-bounds read by passing a malformed ELF file to nvdisasm. A successful exploit of this vulnerability might lead to a partial denial of service.

An out-of-bound read vulnerability in mapToBuffer function in libSDKRecognitionText.spensdk.samsung.so library prior to SMR JAN-2023 Release 1 allows attacker to cause memory access fault.

In affected versions of TensorFlow the tf.raw_ops.DataFormatVecPermute API does not validate the src_format and dst_format attributes. The code assumes that these two arguments define a permutation of NHWC. This can result in uninitialized memory accesses, read outside of bounds and even crashes. This is fixed in versions 1.15.5, 2.0.4, 2.1.3, 2.2.2, 2.3.2, and 2.4.0.

In affected versions of TensorFlow under certain cases, loading a saved model can result in accessing uninitialized memory while building the computation graph. The MakeEdge function creates an edge between one output tensor of the src node (given by output_index) and the input slot of the dst node (given by input_index). This is only possible if the types of the tensors on both sides coincide, so the function begins by obtaining the corresponding DataType values and comparing these for equality. However, there is no check that the indices point to inside of the arrays they index into. Thus, this can result in accessing data out of bounds of the corresponding heap allocated arrays. In most scenarios, this can manifest as unitialized data access, but if the index points far away from the boundaries of the arrays this can be used to leak addresses from the library. This is fixed in versions 1.15.5, 2.0.4, 2.1.3, 2.2.2, 2.3.2, and 2.4.0.

In faceid service, there is a possible out of bounds read due to a missing bounds check. This could lead to local denial of service with System execution privileges needed

Invalid JPEG XL images using libjxl can cause an out of bounds access on a std::vector<std::vector<T>> when rendering splines. The OOB read access can either lead to a segfault, or rendering splines based on other process memory. It is recommended to upgrade past 0.6.0 or patch with https://github.com/libjxl/libjxl/pull/757